diff --git "a/attnserver.run_attnserver.slurm.sh.343197.out.log" "b/attnserver.run_attnserver.slurm.sh.343197.out.log" --- "a/attnserver.run_attnserver.slurm.sh.343197.out.log" +++ "b/attnserver.run_attnserver.slurm.sh.343197.out.log" @@ -11709,3 +11709,6092 @@ make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/mega >>> done with dataset index builder. Compilation time: 0.052 seconds > compiling and loading fused kernels ... INFO:megatron.training.initialize:Setting logging level to 0 +>>> done with compiling and loading fused kernels. Compilation time: 3.292 seconds +time to initialize megatron (seconds): 9.731 +[after megatron is initialized] datetime: 2025-06-21 20:55:55 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 87094784 > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 87094784 + + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 87094784 +>>> embedding + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 87094784 +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 87094784 +>>> embedding +>>> embedding>>> decoder + +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 87094784 +>>> embedding>>> embedding + +>>> decoder +>>> decoder + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 87094784 +>>> output_layer>>> output_layer + + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 87094784 +INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False) +INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1 +Params for bucket 1 (87094784 elements, 87094784 padded size): + module.decoder.final_layernorm.weight + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.weight + module.decoder.layers.0.self_attention.linear_proj.weight + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_qkv.weight + module.decoder.layers.1.self_attention.linear_proj.weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.embedding.word_embeddings.weight + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.decoder.final_layernorm.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.embedding.position_embeddings.weight + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc1.weight +INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=, config_logger_dir='') +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 87094784 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 87094784 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 87094784 +INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine +WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt + will not load any checkpoints and will start from random +(min, max) time across ranks (ms): + load-checkpoint ................................: (69.00, 69.40) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 20:55:56 +> building train, validation, and test datasets ... + > datasets target sizes (minimum size): + train: 10 + validation: 1 + test: 1 +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)] +> building train, validation, and test datasets for GPT ... +INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=4096, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None) +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.005102 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 16648 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.002170 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 16640 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.002030 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 16671 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 20:55:56 +done with setup ... +training ... +Setting rerun_state_machine.current_iteration to 0... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (839.99, 865.92) + train/valid/test-data-iterators-setup ..........: (17.57, 138.04) +[before the start of training step] datetime: 2025-06-21 20:55:56 +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask batch tensor:torch.Size([4, 1, 16384, 16384]) + batch tensor: tokensposition_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp:batch tensor after cp: labels torch.Size([4, 4096])tokens +batch tensor after cp: tokens torch.Size([4, 4096]) + batch tensor after cp: loss_mask torch.Size([4, 4096]) +torch.Size([4, 4096])batch tensor after cp: +attention_mask batch tensor after cp:torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +labels batch tensor after cp: torch.Size([4, 4096])position_ids + batch tensor after cp:torch.Size([4, 4096]) +loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 0 +Done exporting trace 0 +Number of parameters in transformer block in billions: 0.35 +Number of parameters in embedding layers in billions: 0.21 +Total number of parameters in billions: 0.56 +Number of parameters in most loaded shard in billions: 0.0703 +Theoretical memory footprints: weight and optimizer=1206.09 MB +[Rank 1] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4982.0 | max reserved: 4982.0 + [2025-06-21 20:56:09] iteration 1/ 10 | consumed samples: 1 | elapsed time per iteration (ms): 12582.5 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 4294967296.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +[Rank 19] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4706.0 | max reserved: 4706.0[Rank 18] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4930.0 | max reserved: 4930.0 + +[Rank 20] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5122.0 | max reserved: 5122.0[Rank 23] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5122.0 | max reserved: 5122.0 + +[Rank 16] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4706.0 | max reserved: 4706.0 +[Rank 12] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5126.0 | max reserved: 5126.0[Rank 14] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0[Rank 15] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5126.0 | max reserved: 5126.0 + + +[Rank 13] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5126.0 | max reserved: 5126.0[Rank 8] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4998.0 | max reserved: 4998.0 + +[Rank 0] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4982.0 | max reserved: 4982.0 +[Rank 5] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4982.0 | max reserved: 4982.0 +[Rank 25] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0[Rank 29] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0 + +[Rank 17] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4930.0 | max reserved: 4930.0[Rank 21] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5122.0 | max reserved: 5122.0 + +[Rank 9] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5126.0 | max reserved: 5126.0[Rank 10] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5126.0 | max reserved: 5126.0 + +[Rank 7] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4982.0 | max reserved: 4982.0[Rank 3] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4982.0 | max reserved: 4982.0 + +[Rank 24] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0 +[Rank 22] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 5122.0 | max reserved: 5122.0 +[Rank 11] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4998.0 | max reserved: 4998.0 +[Rank 4] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4982.0 | max reserved: 4982.0 +[Rank 27] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0 +[Rank 2] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4982.0 | max reserved: 4982.0 +[Rank 31] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0 +[Rank 6] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4982.0 | max reserved: 4982.0 +[Rank 30] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0 +[Rank 28] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0 +[Rank 26] (after 1 iterations) memory (MB) | allocated: 2416.91455078125 | max allocated: 4549.02197265625 | reserved: 4994.0 | max reserved: 4994.0 +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 1 +Done exporting trace 1 + [2025-06-21 20:56:09] iteration 2/ 10 | consumed samples: 2 | elapsed time per iteration (ms): 276.0 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 2147483648.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_idsbatch tensor: torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) + tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask batch tensor:torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokensattention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384])torch.Size([4, 16384]) + +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp:batch tensor after cp: tokensposition_ids torch.Size([4, 4096])torch.Size([4, 4096]) + +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 2 +Done exporting trace 2 + [2025-06-21 20:56:09] iteration 3/ 10 | consumed samples: 3 | elapsed time per iteration (ms): 174.1 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 1073741824.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 3 +Done exporting trace 3 + [2025-06-21 20:56:09] iteration 4/ 10 | consumed samples: 4 | elapsed time per iteration (ms): 182.7 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 536870912.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp:batch tensor: tokens torch.Size([4, 4096])tokens + batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +torch.Size([4, 16384])batch tensor after cp: +attention_mask batch tensor:torch.Size([4, 1, 4096, 16384]) +labelsbatch tensor after cp: torch.Size([4, 16384])position_ids + batch tensor:torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor:batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +tokens batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 16384])torch.Size([4, 1, 4096, 16384]) + +batch tensor after cp: position_ids batch tensor:torch.Size([4, 4096]) +labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) 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after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 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torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 4 +Done exporting trace 4 + [2025-06-21 20:56:10] iteration 5/ 10 | consumed samples: 5 | elapsed time per iteration (ms): 168.7 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 268435456.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) 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torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: 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16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 5 +Done exporting trace 5 + [2025-06-21 20:56:10] iteration 6/ 10 | consumed samples: 6 | elapsed time per iteration (ms): 201.1 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 134217728.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_maskbatch tensor: torch.Size([4, 1, 16384, 16384]) + batch tensor:tokens position_ids torch.Size([4, 16384]) +torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) 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position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) 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torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 6 +Done exporting trace 6 + [2025-06-21 20:56:10] iteration 7/ 10 | consumed samples: 7 | elapsed time per iteration (ms): 179.5 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 67108864.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384])batch tensor after cp: +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) + tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens batch tensor: torch.Size([4, 16384])tokens +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384])torch.Size([4, 16384]) +batch tensor: +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) + loss_mask batch tensor:torch.Size([4, 16384]) +labels torch.Size([4, 16384])batch tensor: + batch tensor:attention_mask loss_mask torch.Size([4, 16384]) +torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor:batch tensor: attention_maskposition_ids torch.Size([4, 16384])torch.Size([4, 1, 16384, 16384]) + +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp:batch tensor after cp: position_ids tokenstorch.Size([4, 4096]) +torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 7 +Done exporting trace 7 + [2025-06-21 20:56:10] iteration 8/ 10 | consumed samples: 8 | elapsed time per iteration (ms): 167.2 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 33554432.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 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tensor:tokens attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: torch.Size([4, 16384])position_ids +torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp:batch tensor after cp: tokenstokens torch.Size([4, 4096])torch.Size([4, 4096]) + +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp:batch tensor after cp: labelslabels torch.Size([4, 4096])torch.Size([4, 4096]) + +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp:batch tensor after cp: loss_masktokens torch.Size([4, 4096]) +batch tensor after cp:batch tensor after cp: loss_maskloss_mask torch.Size([4, 4096])torch.Size([4, 4096]) + +batch tensor after cp:batch tensor after cp: attention_maskattention_mask torch.Size([4, 1, 4096, 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tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: 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tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: 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tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) + [2025-06-21 20:56:10] iteration 9/ 10 | consumed samples: 9 | elapsed time per iteration (ms): 184.0 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 16777216.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch 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tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask 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labels torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 9 +Done exporting trace 9 + [2025-06-21 20:56:11] iteration 10/ 10 | consumed samples: 10 | elapsed time per iteration (ms): 166.2 | learning rate: 0.000000E+00 | global batch size: 1 | loss scale: 8388608.0 | number of skipped iterations: 1 | number of nan iterations: 0 | +[after training is done] datetime: 2025-06-21 20:56:11 +saving checkpoint at iteration 10 to gpt-checkpoint in torch_dist format +DEBUG:megatron.training.checkpointing:rank: 17, takes 0.028894662857055664 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 20, takes 0.028990983963012695 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 23, takes 0.02904033660888672 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 25, takes 0.02939438819885254 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 29, takes 0.02942681312561035 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 22, takes 0.029087066650390625 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.02953171730041504 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.029515504837036133 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 30, takes 0.029445886611938477 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 18, takes 0.029124975204467773 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.02955174446105957 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.03108692169189453 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.031093358993530273 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 27, takes 0.02945089340209961 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 19, takes 0.030231475830078125 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.029615163803100586 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.031155109405517578 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 31, takes 0.029464006423950195 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 28, takes 0.029488086700439453 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 16, takes 0.030245065689086914 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.03027653694152832 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.031136751174926758 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 24, takes 0.030055761337280273 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.031147003173828125 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.031203746795654297 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.031209945678710938 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.031265974044799805 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.0312650203704834 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.031438350677490234 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 26, takes 0.03286600112915039 to prepare state dict for ckpt +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.034010887145996094 to prepare state dict for ckpt +WARNING:megatron.core.dist_checkpointing.serialization:Overwriting old incomplete / corrupted checkpoint... +DEBUG:megatron.training.checkpointing:rank: 21, takes 0.04010367393493652 to prepare state dict for ckpt +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(104857600), 0), (np.int64(106954752), 1), (np.int64(106954752), 2), (np.int64(102794240), 3)] +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:Apply save parallelization +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(130149376), 0), (np.int64(132120576), 1), (np.int64(130149376), 2), (np.int64(130149376), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(130149376), 0), (np.int64(132120576), 1), (np.int64(130149376), 2), (np.int64(130149376), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(130149376), 0), (np.int64(132120576), 1), (np.int64(130149376), 2), (np.int64(130149376), 3)] +DEBUG:megatron.core.dist_checkpointing.exchange_utils:distribute_shards_to_ranks distribution: [(np.int64(130149376), 0), (np.int64(132120576), 1), (np.int64(130149376), 2), (np.int64(130149376), 3)] +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save sharding, time: 1.1909472942352295 +DEBUG:megatron.core.dist_checkpointing.strategies.fully_parallel:parallel save 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takes 1.621246337890625e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 25, takes 1.6927719116210938e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 18, takes 1.8596649169921875e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 20, takes 2.1457672119140625e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 27, takes 1.621246337890625e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 22, takes 1.71661376953125e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 28, takes 1.71661376953125e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 19, takes 1.811981201171875e-05 to finish D2H +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:rank: 17, takes 1.9311904907226562e-05 to finish D2H 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consumed: 59834368, before: 1947619328, after: 2007453696 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.41s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9404697, rank: 18, write(sync,parallel): 0.35219573974609375 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9407818, rank: 11, write(sync,parallel): 0.35115623474121094 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9416392, rank: 20, write(sync,parallel): 0.35088467597961426 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.41s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9432826, rank: 25, write(sync,parallel): 0.3512997627258301 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 57737216, before: 1947619328, after: 2005356544 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9448779, rank: 14, write(sync,parallel): 0.3602614402770996 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 59809792, before: 1944645632, after: 2004455424 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9464517, rank: 26, write(sync,parallel): 0.350677490234375 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109637632, before: 1944477696, after: 2054115328 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 59838464, before: 1976868864, after: 2036707328 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109510656, before: 1827483648, after: 1936994304 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109694976, before: 1922109440, after: 2031804416 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9469867, rank: 12, write(sync,parallel): 0.3646407127380371 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9495087, rank: 5, write(sync,parallel): 0.40580058097839355 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 57757696, before: 1976868864, after: 2034626560 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.42s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9522686, rank: 28, write(sync,parallel): 0.3738827705383301 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9525762, rank: 31, write(sync,parallel): 0.37379908561706543 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9570673, rank: 27, write(sync,parallel): 0.36633825302124023 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9584014, rank: 13, write(sync,parallel): 0.37598538398742676 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9607728, rank: 6, write(sync,parallel): 0.4087388515472412 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.960996, rank: 3, write(sync,parallel): 0.4140477180480957 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 68321280, before: 1941262336, after: 2009583616 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9632132, rank: 1, write(sync,parallel): 0.4161796569824219 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9644444, rank: 19, write(sync,parallel): 0.384152889251709 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9716475, rank: 8, write(sync,parallel): 0.37660789489746094 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9755437, rank: 4, write(sync,parallel): 0.42798447608947754 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9756331, rank: 2, write(sync,parallel): 0.42690372467041016 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1 consumed: 59736064, before: 1936650240, after: 1996386304 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:0 consumed: 109391872, before: 2134151168, after: 2243543040 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.98409, rank: 7, write(sync,parallel): 0.4290769100189209 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.45s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539372.9952419, rank: 16, write(sync,parallel): 0.4030623435974121 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.45s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.48s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.48s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539373.001212, rank: 15, write(sync,parallel): 0.42696428298950195 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.46s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.48s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539373.0118482, rank: 23, write(sync,parallel): 0.44132256507873535 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539373.0150943, rank: 0, write(sync,parallel): 0.43109655380249023 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.49s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.47s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539373.0167603, rank: 17, write(sync,parallel): 0.42436838150024414 +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539373.019088, rank: 24, write(sync,parallel): 0.42099976539611816 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.48s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.49s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:FileSystemWriterAsync: collected worker results successfully +DEBUG:megatron.core.dist_checkpointing.strategies.filesystem_async:1750539373.0334282, rank: 9, write(sync,parallel): 0.44910526275634766 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.50s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.50s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.52s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.54s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.53s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.52s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:TemporalAsyncCaller: Async process join finished after 0.55s from forking +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0962498, 1, gather: 0.10584521293640137 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0957634, 9, gather: 0.002629995346069336 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0964503, 6, gather: 0.10181689262390137 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096454, 4, gather: 0.0937795639038086 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0964608, 7, gather: 0.07897210121154785 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096231, 30, gather: 0.14315533638000488 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0959568, 12, gather: 0.08981609344482422 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0964847, 2, gather: 0.09471416473388672 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0962598, 28, gather: 0.09154748916625977 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096065, 23, gather: 0.030863523483276367 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0960698, 20, gather: 0.09687161445617676 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096089, 19, gather: 0.07263636589050293 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0961027, 16, gather: 0.04362750053405762 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0961585, 17, gather: 0.027774572372436523 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0961778, 22, gather: 0.09928417205810547 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0959675, 10, gather: 0.08510160446166992 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096486, 3, gather: 0.10849404335021973 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096267, 29, gather: 0.1528165340423584 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0962338, 21, gather: 0.15454912185668945 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0960126, 15, gather: 0.039202213287353516 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0965025, 5, gather: 0.12128758430480957 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0962875, 24, gather: 0.022193431854248047 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0963163, 18, gather: 0.10159754753112793 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096039, 13, gather: 0.0809626579284668 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0993726, 0, gather: 0.05018115043640137 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0963132, 27, gather: 0.0834052562713623 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0960317, 11, gather: 0.10197281837463379 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0964088, 25, gather: 0.09725809097290039 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096135, 8, gather: 0.0801706314086914 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0964816, 31, gather: 0.08906865119934082 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.096556, 26, gather: 0.09652280807495117 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.0964174, 14, gather: 0.07073092460632324 +DEBUG:megatron.core.dist_checkpointing.strategies.state_dict_saver:1750539373.1117163, metadata_write: 0.012224912643432617 +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0652s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1234s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1122s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1110s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1192s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0966s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1257s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1097s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0401s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1140s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1149s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1608s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1065s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1014s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0903s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1172s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1146s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1722s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0486s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0614s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0989s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0974s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0205s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0568s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1027s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1078s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1197s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1385s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1702s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0453s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.1190s +DEBUG:megatron.core.dist_checkpointing.strategies.async_utils:finalize took 0.0881s + successfully saved checkpoint from iteration 10 to gpt-checkpoint [ t 1/8, p 1/1 ] +DEBUG:megatron.training.checkpointing:rank: 24, takes 0.002377748489379883 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 26, takes 0.002393484115600586 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 27, takes 0.002367258071899414 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 28, takes 0.0023813247680664062 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 30, takes 0.002369403839111328 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 25, takes 0.002380847930908203 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 31, takes 0.0023756027221679688 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 19, takes 0.0023849010467529297 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 20, takes 0.002393007278442383 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 16, takes 0.002370119094848633 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 17, takes 0.0023956298828125 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 8, takes 0.002405405044555664 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 14, takes 0.002386331558227539 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 12, takes 0.002395153045654297 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 15, takes 0.0024008750915527344 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 13, takes 0.002373933792114258 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 9, takes 0.002386808395385742 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 4, takes 0.002405405044555664 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 6, takes 0.0023953914642333984 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 1, takes 0.002435922622680664 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 3, takes 0.0024154186248779297 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 7, takes 0.0024139881134033203 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 2, takes 0.0024178028106689453 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 0, takes 0.002434253692626953 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 5, takes 0.0024187564849853516 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 29, takes 0.00237274169921875 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 18, takes 0.002339601516723633 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 10, takes 0.0023636817932128906 to finalize ckpt save +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +DEBUG:megatron.training.checkpointing:rank: 22, takes 0.002443552017211914 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 23, takes 0.0024178028106689453 to finalize ckpt save +DEBUG:megatron.training.checkpointing:rank: 11, takes 0.0023620128631591797 to finalize ckpt save +Evaluating on 1 samples +Evaluating iter 1/1 +DEBUG:megatron.training.checkpointing:rank: 21, takes 0.0023763179779052734 to finalize ckpt save +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 10 +Done exporting trace 10 +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +(min, max) time across ranks (ms): + evaluate .......................................: (3403.95, 3406.79) +---------------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on validation set | lm loss value: 1.248800E+01 | lm loss PPL: 2.651372E+05 | +---------------------------------------------------------------------------------------------------------------- +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +Evaluating on 1 samples +Evaluating iter 1/1 +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +batch tensor: tokens torch.Size([4, 16384]) +batch tensor: labels torch.Size([4, 16384]) +batch tensor: loss_mask torch.Size([4, 16384]) +batch tensor: attention_mask torch.Size([4, 1, 16384, 16384]) +batch tensor: position_ids torch.Size([4, 16384]) +batch tensor after cp: tokens torch.Size([4, 4096]) +batch tensor after cp: labels torch.Size([4, 4096]) +batch tensor after cp: loss_mask torch.Size([4, 4096]) +batch tensor after cp: attention_mask torch.Size([4, 1, 4096, 16384]) +batch tensor after cp: position_ids torch.Size([4, 4096]) +Start exporting trace 11 +Done exporting trace 11 +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +(min, max) time across ranks (ms): + evaluate .......................................: (170.01, 173.02) +WARNING:megatron.core.rerun_state_machine:Setting RerunStateMachine mode RerunMode.DISABLED +---------------------------------------------------------------------------------------------------------- + validation loss at iteration 10 on test set | lm loss value: 1.248800E+01 | lm loss PPL: 2.651372E+05 | +---------------------------------------------------------------------------------------------------------- +Running ctx_length=8192, TP_SIZE=8, CP_SIZE=4, BATCH_SIZE=4 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 8192 +TP_SIZE: 8 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: gpt-checkpoint +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 8192 +TP_SIZE: 8 +CP_SIZE: 4 +-------------------------------- +CTX_LENGTH: 8192 +TP_SIZE: 8 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +CHECKPOINT_PATH: gpt-checkpoint +-------------------------------- +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +Cleaning up checkpoint directory: gpt-checkpoint +-------------------------------- +CTX_LENGTH: 8192 +TP_SIZE: 8 +CP_SIZE: 4 +CHECKPOINT_PATH: gpt-checkpoint +PWD: /mnt/weka/home/hao.zhang/junda/attnserver-megatron +-------------------------------- +/mnt/weka/home/hao.zhang/conda/miniconda/envs/junda-attnserver/bin/python3 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +using world size: 32, data-parallel size: 1, context-parallel size: 4, hierarchical context-parallel sizes: Nonetensor-model-parallel size: 8, encoder-tensor-model-parallel size: 0, pipeline-model-parallel size: 1, encoder-pipeline-model-parallel size: 0 +Number of virtual stages per pipeline stage: None +WARNING: Setting args.check_for_nan_in_loss_and_grad to False since dynamic loss scaling is being used +using torch.float16 for parameters ... +------------------------ arguments ------------------------ + account_for_embedding_in_pipeline_split ......... False + account_for_loss_in_pipeline_split .............. False + accumulate_allreduce_grads_in_fp32 .............. False + adam_beta1 ...................................... 0.9 + adam_beta2 ...................................... 0.999 + adam_eps ........................................ 1e-08 + add_bias_linear ................................. True + add_position_embedding .......................... True + add_qkv_bias .................................... True + adlr_autoresume ................................. False + adlr_autoresume_interval ........................ 1000 + align_grad_reduce ............................... True + align_param_gather .............................. False + app_tag_run_name ................................ None + app_tag_run_version ............................. 0.0.0 + apply_layernorm_1p .............................. False + apply_query_key_layer_scaling ................... False + apply_residual_connection_post_layernorm ........ False + apply_rope_fusion ............................... False + async_save ...................................... None + async_tensor_model_parallel_allreduce ........... True + attention_backend ............................... AttnBackend.auto + attention_dropout ............................... 0.1 + attention_softmax_in_fp32 ....................... False + auto_detect_ckpt_format ......................... False + barrier_with_L1_time ............................ True + bert_binary_head ................................ True + bert_embedder_type .............................. megatron + bert_load ....................................... None + bf16 ............................................ False + bias_dropout_fusion ............................. True + bias_gelu_fusion ................................ True + bias_swiglu_fusion .............................. True + biencoder_projection_dim ........................ 0 + biencoder_shared_query_context_model ............ False + block_data_path ................................. None + calc_ft_timeouts ................................ False + calculate_per_token_loss ........................ False + check_for_large_grads ........................... False + check_for_nan_in_loss_and_grad .................. False + check_for_spiky_loss ............................ False + check_weight_hash_across_dp_replicas_interval ... None + ckpt_assume_constant_structure .................. False + ckpt_convert_format ............................. None + ckpt_convert_save ............................... None + ckpt_convert_update_legacy_dist_opt_format ...... False + ckpt_format ..................................... torch_dist + ckpt_fully_parallel_load ........................ False + ckpt_fully_parallel_save ........................ True + ckpt_fully_parallel_save_deprecated ............. False + ckpt_step ....................................... None + classes_fraction ................................ 1.0 + clip_grad ....................................... 1.0 + clone_scatter_output_in_embedding ............... True + config_logger_dir ............................... + consumed_train_samples .......................... 0 + consumed_valid_samples .......................... 0 + context_parallel_size ........................... 4 + cp_comm_type .................................... ['p2p'] + create_attention_mask_in_dataloader ............. True + cross_entropy_fusion_impl ....................... native + cross_entropy_loss_fusion ....................... False + cuda_graph_scope ................................ full + cuda_graph_warmup_steps ......................... 3 + data_args_path .................................. None + data_cache_path ................................. None + data_parallel_random_init ....................... False + data_parallel_sharding_strategy ................. no_shard + data_parallel_size .............................. 1 + data_path ....................................... None + data_per_class_fraction ......................... 1.0 + data_sharding ................................... True + dataloader_type ................................. single + ddp_average_in_collective ....................... False + ddp_bucket_size ................................. None + ddp_num_buckets ................................. None + ddp_pad_buckets_for_high_nccl_busbw ............. False + decoder_first_pipeline_num_layers ............... None + decoder_last_pipeline_num_layers ................ None + decoder_num_layers .............................. None + decoder_seq_length .............................. None + decoupled_lr .................................... None + decoupled_min_lr ................................ None + decrease_batch_size_if_needed ................... False + defer_embedding_wgrad_compute ................... False + deprecated_use_mcore_models ..................... False + deterministic_mode .............................. False + dino_bottleneck_size ............................ 256 + dino_freeze_last_layer .......................... 1 + dino_head_hidden_size ........................... 2048 + dino_local_crops_number ......................... 10 + dino_local_img_size ............................. 96 + dino_norm_last_layer ............................ False + dino_teacher_temp ............................... 0.07 + dino_warmup_teacher_temp ........................ 0.04 + dino_warmup_teacher_temp_epochs ................. 30 + disable_bf16_reduced_precision_matmul ........... False + disable_mamba_mem_eff_path ...................... False + disable_straggler_on_startup .................... False + dist_ckpt_format_deprecated ..................... None + dist_ckpt_strictness ............................ assume_ok_unexpected + distribute_saved_activations .................... False + distributed_backend ............................. nccl + distributed_timeout_minutes ..................... 10 + embedding_path .................................. None + empty_unused_memory_level ....................... 0 + enable_cuda_graph ............................... False + enable_ft_package ............................... False + enable_gloo_process_groups ...................... True + enable_msc ...................................... True + enable_one_logger ............................... True + encoder_num_layers .............................. 2 + encoder_pipeline_model_parallel_size ............ 0 + encoder_seq_length .............................. 8192 + encoder_tensor_model_parallel_size .............. 0 + end_weight_decay ................................ 0.1 + eod_mask_loss ................................... False + error_injection_rate ............................ 0 + error_injection_type ............................ transient_error + eval_interval ................................... 16 + eval_iters ...................................... 1 + evidence_data_path .............................. None + exit_duration_in_mins ........................... None + exit_interval ................................... None + exit_on_missing_checkpoint ...................... False + exit_signal_handler ............................. False + exp_avg_dtype ................................... torch.float32 + exp_avg_sq_dtype ................................ torch.float32 + expert_model_parallel_size ...................... 1 + expert_tensor_parallel_size ..................... 8 + external_cuda_graph ............................. False + ffn_hidden_size ................................. 16384 + finetune ........................................ False + first_last_layers_bf16 .......................... False + flash_decode .................................... False + fp16 ............................................ True + fp16_lm_cross_entropy ........................... False + fp32_residual_connection ........................ False + fp8 ............................................. None + fp8_amax_compute_algo ........................... most_recent + fp8_amax_history_len ............................ 1 + fp8_interval .................................... 1 + fp8_margin ...................................... 0 + fp8_param_gather ................................ False + fp8_recipe ...................................... delayed + fp8_wgrad ....................................... True + fsdp_double_buffer .............................. False + global_batch_size ............................... 1 + grad_reduce_in_bf16 ............................. False + gradient_accumulation_fusion .................... True + gradient_reduce_div_fusion ...................... True + group_query_attention ........................... True + head_lr_mult .................................... 1.0 + heterogeneous_layers_config_encoded_json ........ None + heterogeneous_layers_config_path ................ None + hidden_dropout .................................. 0.1 + hidden_size ..................................... 4096 + hierarchical_context_parallel_sizes ............. None + high_priority_stream_groups ..................... [] + hybrid_attention_ratio .......................... 0.0 + hybrid_mlp_ratio ................................ 0.0 + hybrid_override_pattern ......................... None + hysteresis ...................................... 2 + ict_head_size ................................... None + ict_load ........................................ None + img_h ........................................... 224 + img_w ........................................... 224 + indexer_batch_size .............................. 128 + indexer_log_interval ............................ 1000 + inference_batch_times_seqlen_threshold .......... -1 + inference_dynamic_batching ...................... False + inference_dynamic_batching_buffer_guaranteed_fraction 0.2 + inference_dynamic_batching_buffer_overflow_factor None + inference_dynamic_batching_buffer_size_gb ....... 40.0 + inference_dynamic_batching_chunk_size ........... 256 + inference_dynamic_batching_max_requests_override None + inference_dynamic_batching_max_tokens_override .. None + inference_max_batch_size ........................ 8 + inference_max_seq_length ........................ 2560 + inference_rng_tracker ........................... False + init_method_std ................................. 0.02 + init_method_xavier_uniform ...................... False + init_model_with_meta_device ..................... False + initial_loss_scale .............................. 4294967296 + inprocess_active_world_size ..................... 32 + inprocess_barrier_timeout ....................... 120 + inprocess_completion_timeout .................... 120 + inprocess_empty_cuda_cache ...................... False + inprocess_granularity ........................... node + inprocess_hard_timeout .......................... 90 + inprocess_heartbeat_interval .................... 30 + inprocess_heartbeat_timeout ..................... 60 + inprocess_last_call_wait ........................ 1 + inprocess_max_iterations ........................ None + inprocess_monitor_process_interval .............. 1.0 + inprocess_monitor_thread_interval ............... 1.0 + inprocess_progress_watchdog_interval ............ 1.0 + inprocess_restart ............................... False + inprocess_soft_timeout .......................... 60 + inprocess_termination_grace_time ................ 1 + is_hybrid_model ................................. False + iter_per_epoch .................................. 1250 + iterations_to_skip .............................. [] + keep_fp8_transpose_cache_when_using_custom_fsdp . False + kv_channels ..................................... 64 + kv_lora_rank .................................... 32 + lazy_mpu_init ................................... None + load ............................................ gpt-checkpoint + load_model_opt_format ........................... False + local_rank ...................................... 0 + log_interval .................................... 1 + log_loss_scale_to_tensorboard ................... True + log_memory_to_tensorboard ....................... False + log_num_zeros_in_grad ........................... False + log_params_norm ................................. False + log_progress .................................... False + log_straggler ................................... False + log_throughput .................................. False + log_timers_to_tensorboard ....................... False + log_validation_ppl_to_tensorboard ............... False + log_world_size_to_tensorboard ................... False + logging_level ................................... 0 + loss_scale ...................................... None + loss_scale_window ............................... 1000 + lr .............................................. 0.0005 + lr_decay_iters .................................. 150000 + lr_decay_samples ................................ None + lr_decay_style .................................. cosine + lr_warmup_fraction .............................. None + lr_warmup_init .................................. 0.0 + lr_warmup_iters ................................. 2 + lr_warmup_samples ............................... 0 + lr_wsd_decay_iters .............................. None + lr_wsd_decay_samples ............................ None + lr_wsd_decay_style .............................. exponential + main_grads_dtype ................................ torch.float32 + main_params_dtype ............................... torch.float32 + make_vocab_size_divisible_by .................... 128 + mamba_head_dim .................................. 64 + mamba_num_groups ................................ 8 + mamba_num_heads ................................. None + mamba_state_dim ................................. 128 + manual_gc ....................................... False + manual_gc_eval .................................. True + manual_gc_interval .............................. 0 + mask_factor ..................................... 1.0 + mask_prob ....................................... 0.15 + mask_type ....................................... random + masked_softmax_fusion ........................... True + max_position_embeddings ......................... 8192 + max_tokens_to_oom ............................... 12000 + memory_snapshot_path ............................ snapshot.pickle + merge_file ...................................... merges.txt + micro_batch_size ................................ 1 + microbatch_group_size_per_vp_stage .............. None + mid_level_dataset_surplus ....................... 0.005 + min_loss_scale .................................. 1.0 + min_lr .......................................... 0.0 + mlp_chunks_for_prefill .......................... 1 + mmap_bin_files .................................. True + mock_data ....................................... True + moe_apply_probs_on_input ........................ False + moe_aux_loss_coeff .............................. 0.0 + moe_enable_deepep ............................... False + moe_expert_capacity_factor ...................... None + moe_extended_tp ................................. False + moe_ffn_hidden_size ............................. None + moe_grouped_gemm ................................ False + moe_input_jitter_eps ............................ None + moe_layer_freq .................................. 1 + moe_layer_recompute ............................. False + moe_pad_expert_input_to_capacity ................ False + moe_per_layer_logging ........................... False + moe_permute_fusion .............................. False + moe_router_bias_update_rate ..................... 0.001 + moe_router_dtype ................................ None + moe_router_enable_expert_bias ................... False + moe_router_force_load_balancing ................. False + moe_router_group_topk ........................... None + moe_router_load_balancing_type .................. aux_loss + moe_router_num_groups ........................... None + moe_router_padding_for_fp8 ...................... False + moe_router_pre_softmax .......................... False + moe_router_score_function ....................... softmax + moe_router_topk ................................. 2 + moe_router_topk_scaling_factor .................. None + moe_shared_expert_intermediate_size ............. None + moe_shared_expert_overlap ....................... False + moe_token_dispatcher_type ....................... allgather + moe_token_drop_policy ........................... probs + moe_use_legacy_grouped_gemm ..................... False + moe_use_upcycling ............................... False + moe_z_loss_coeff ................................ None + mrope_section ................................... None + mscale .......................................... 1.0 + mscale_all_dim .................................. 1.0 + mtp_loss_scaling_factor ......................... 0.1 + mtp_num_layers .................................. None + multi_latent_attention .......................... False + nccl_all_reduce_for_prefill ..................... False + nccl_communicator_config_path ................... None + nccl_ub ......................................... False + no_load_optim ................................... None + no_load_rng ..................................... None + no_persist_layer_norm ........................... False + no_rope_freq .................................... None + no_save_optim ................................... None + no_save_rng ..................................... None + non_persistent_ckpt_type ........................ None + non_persistent_global_ckpt_dir .................. None + non_persistent_local_ckpt_algo .................. fully_parallel + non_persistent_local_ckpt_dir ................... None + non_persistent_save_interval .................... None + norm_epsilon .................................... 1e-05 + normalization ................................... LayerNorm + num_attention_heads ............................. 64 + num_channels .................................... 3 + num_classes ..................................... 1000 + num_dataset_builder_threads ..................... 1 + num_distributed_optimizer_instances ............. 1 + num_experts ..................................... None + num_layers ...................................... 2 + num_layers_at_end_in_bf16 ....................... 1 + num_layers_at_start_in_bf16 ..................... 1 + num_layers_per_virtual_pipeline_stage ........... None + num_query_groups ................................ 16 + num_virtual_stages_per_pipeline_rank ............ None + num_workers ..................................... 2 + object_storage_cache_path ....................... None + one_logger_async ................................ False + one_logger_project .............................. megatron-lm + one_logger_run_name ............................. None + onnx_safe ....................................... None + openai_gelu ..................................... False + optimizer ....................................... adam + optimizer_cpu_offload ........................... False + optimizer_offload_fraction ...................... 1.0 + output_bert_embeddings .......................... False + overlap_cpu_optimizer_d2h_h2d ................... False + overlap_grad_reduce ............................. False + overlap_p2p_comm ................................ False + overlap_p2p_comm_warmup_flush ................... False + overlap_param_gather ............................ False + overlap_param_gather_with_optimizer_step ........ False + override_opt_param_scheduler .................... False + params_dtype .................................... torch.float16 + patch_dim ....................................... 16 + per_split_data_args_path ........................ None + perform_initialization .......................... True + pin_cpu_grads ................................... True + pin_cpu_params .................................. True + pipeline_model_parallel_comm_backend ............ None + pipeline_model_parallel_size .................... 1 + pipeline_model_parallel_split_rank .............. None + position_embedding_type ......................... learned_absolute + pretrained_checkpoint ........................... None + profile ......................................... False + profile_ranks ................................... [0] + profile_step_end ................................ 12 + profile_step_start .............................. 10 + q_lora_rank ..................................... None + qk_head_dim ..................................... 128 + qk_l2_norm ...................................... False + qk_layernorm .................................... False + qk_pos_emb_head_dim ............................. 64 + query_in_block_prob ............................. 0.1 + rampup_batch_size ............................... None + rank ............................................ 0 + recompute_granularity ........................... None + recompute_method ................................ None + recompute_modules ............................... None + recompute_num_layers ............................ None + record_memory_history ........................... False + relative_attention_max_distance ................. 128 + relative_attention_num_buckets .................. 32 + replication ..................................... False + replication_factor .............................. 2 + replication_jump ................................ None + rerun_mode ...................................... disabled + reset_attention_mask ............................ False + reset_position_ids .............................. False + result_rejected_tracker_filename ................ None + retriever_report_topk_accuracies ................ [] + retriever_score_scaling ......................... False + retriever_seq_length ............................ 256 + retro_add_retriever ............................. False + retro_attention_gate ............................ 1 + retro_cyclic_train_iters ........................ None + retro_encoder_attention_dropout ................. 0.1 + retro_encoder_hidden_dropout .................... 0.1 + retro_encoder_layers ............................ 2 + retro_num_neighbors ............................. 2 + retro_num_retrieved_chunks ...................... 2 + retro_project_dir ............................... None + retro_verify_neighbor_count ..................... True + rope_scaling_factor ............................. 8.0 + rotary_base ..................................... 10000 + rotary_interleaved .............................. False + rotary_percent .................................. 1.0 + rotary_scaling_factor ........................... 1.0 + rotary_seq_len_interpolation_factor ............. None + run_workload_inspector_server ................... False + sample_rate ..................................... 1.0 + save ............................................ gpt-checkpoint + save_interval ................................... 16 + scatter_gather_tensors_in_pipeline .............. True + seed ............................................ 1234 + seq_length ...................................... 8192 + sequence_parallel ............................... False + sgd_momentum .................................... 0.9 + short_seq_prob .................................. 0.1 + skip_train ...................................... False + skipped_train_samples ........................... 0 + spec ............................................ None + split ........................................... None + squared_relu .................................... False + start_weight_decay .............................. 0.1 + straggler_ctrlr_port ............................ 65535 + straggler_minmax_count .......................... 1 + suggested_communication_unit_size ............... None + swiglu .......................................... False + swin_backbone_type .............................. tiny + symmetric_ar_type ............................... None + te_rng_tracker .................................. False + tensor_model_parallel_size ...................... 8 + tensorboard_dir ................................. tensorboard-logs/ + tensorboard_log_interval ........................ 1 + tensorboard_queue_size .......................... 1000 + test_data_path .................................. None + test_mode ....................................... False + tiktoken_num_special_tokens ..................... 1000 + tiktoken_pattern ................................ None + tiktoken_special_tokens ......................... None + timing_log_level ................................ 0 + timing_log_option ............................... minmax + titles_data_path ................................ None + tokenizer_model ................................. None + tokenizer_type .................................. GPT2BPETokenizer + torch_fsdp2_reshard_after_forward ............... True + tp_comm_bootstrap_backend ....................... nccl + tp_comm_bulk_dgrad .............................. True + tp_comm_bulk_wgrad .............................. True + tp_comm_overlap ................................. False + tp_comm_overlap_ag .............................. True + tp_comm_overlap_cfg ............................. None + tp_comm_overlap_rs .............................. True + tp_comm_overlap_rs_dgrad ........................ False + tp_comm_split_ag ................................ True + tp_comm_split_rs ................................ True + train_data_path ................................. None + train_iters ..................................... 10 + train_samples ................................... None + train_sync_interval ............................. None + transformer_impl ................................ transformer_engine + transformer_pipeline_model_parallel_size ........ 1 + untie_embeddings_and_output_weights ............. False + use_checkpoint_args ............................. False + use_checkpoint_opt_param_scheduler .............. False + use_cpu_initialization .......................... None + use_custom_fsdp ................................. False + use_dist_ckpt ................................... True + use_dist_ckpt_deprecated ........................ False + use_distributed_optimizer ....................... False + use_flash_attn .................................. False + use_legacy_models ............................... False + use_mp_args_from_checkpoint_args ................ False + use_one_sent_docs ............................... False + use_persistent_ckpt_worker ...................... False + use_precision_aware_optimizer ................... False + use_pytorch_profiler ............................ False + use_ring_exchange_p2p ........................... False + use_rope_scaling ................................ False + use_rotary_position_embeddings .................. False + use_sharp ....................................... False + use_tokenizer_model_from_checkpoint_args ........ True + use_torch_fsdp2 ................................. False + use_torch_optimizer_for_cpu_offload ............. False + use_tp_pp_dp_mapping ............................ False + v_head_dim ...................................... 128 + valid_data_path ................................. None + variable_seq_lengths ............................ False + virtual_pipeline_model_parallel_size ............ None + vision_backbone_type ............................ vit + vision_pretraining .............................. False + vision_pretraining_type ......................... classify + vocab_extra_ids ................................. 0 + vocab_file ...................................... vocab.json + vocab_size ...................................... None + wandb_exp_name .................................. + wandb_project ................................... + wandb_save_dir .................................. + weight_decay .................................... 0.1 + weight_decay_incr_style ......................... constant + wgrad_deferral_limit ............................ 0 + world_size ...................................... 32 + yaml_cfg ........................................ None +-------------------- end of arguments --------------------- +INFO:megatron.core.num_microbatches_calculator:setting number of microbatches to constant 1 +> building GPT2BPETokenizer tokenizer ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 + > padded vocab (size: 50257) with 943 dummy tokens (new size: 51200) +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING:megatron.core.rerun_state_machine:RerunStateMachine initialized in mode RerunMode.DISABLED +> initializing torch distributed ... +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +WARNING: TensorBoard writing requested but is not available (are you using PyTorch 1.1.0 or later?), no TensorBoard logs will be written. +WARNING: one_logger package is required to enable e2e metrics tracking. please go to https://confluence.nvidia.com/display/MLWFO/Package+Repositories for details to install it +INFO:megatron.training.initialize:Setting logging level to 0 +> initialized tensor model parallel with size 8 +> initialized pipeline model parallel with size 1 +> setting random seeds to 1234 ... +INFO:megatron.training.initialize:Setting logging level to 0 +> compiling dataset index builder ... +make: Entering directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +INFO:megatron.training.initialize:Setting logging level to 0 +make: Nothing to be done for 'default'. +make: Leaving directory '/mnt/weka/home/hao.zhang/junda/attnserver-megatron/megatron/core/datasets' +>>> done with dataset index builder. Compilation time: 0.043 seconds +> compiling and loading fused kernels ... +>>> done with compiling and loading fused kernels. Compilation time: 3.430 seconds +time to initialize megatron (seconds): 9.865 +[after megatron is initialized] datetime: 2025-06-21 20:56:55 +building GPT model ... +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding>>> embedding + +>>> decoder>>> decoder + +>>> output_layer>>> output_layer + + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 103872000 +>>> embedding>>> embedding + +>>> decoder>>> decoder + +>>> output_layer>>> output_layer + +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (7, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (6, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (4, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (5, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (1, 0): 103872000 +INFO:megatron.core.distributed.distributed_data_parallel:Setting up DistributedDataParallel with config DistributedDataParallelConfig(grad_reduce_in_fp32=False, overlap_grad_reduce=False, overlap_param_gather=False, align_param_gather=False, use_distributed_optimizer=False, num_distributed_optimizer_instances=1, check_for_nan_in_grad=False, check_for_large_grads=False, bucket_size=None, pad_buckets_for_high_nccl_busbw=False, average_in_collective=False, fp8_param_gather=False, use_custom_fsdp=False, data_parallel_sharding_strategy='no_shard', gradient_reduce_div_fusion=True, suggested_communication_unit_size=None, preserve_fp32_weights=True, keep_fp8_transpose_cache_when_using_custom_fsdp=False, nccl_ub=False, fsdp_double_buffer=False) +INFO:megatron.core.distributed.param_and_grad_buffer:Number of buckets for gradient all-reduce / reduce-scatter: 1 +Params for bucket 1 (103872000 elements, 103872000 padded size): + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_weight + module.embedding.word_embeddings.weight + module.decoder.layers.1.mlp.linear_fc1.bias + module.decoder.final_layernorm.weight + module.decoder.layers.0.mlp.linear_fc1.bias + module.decoder.layers.0.self_attention.linear_proj.weight + module.decoder.layers.1.self_attention.linear_qkv.weight + module.decoder.layers.1.self_attention.linear_proj.weight + module.decoder.layers.0.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc2.weight + module.decoder.layers.1.self_attention.linear_proj.bias + module.decoder.layers.1.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_bias + module.decoder.layers.0.self_attention.linear_qkv.weight + module.decoder.layers.1.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.self_attention.linear_qkv.bias + module.decoder.layers.0.mlp.linear_fc2.bias + module.decoder.layers.0.mlp.linear_fc1.layer_norm_weight + module.decoder.layers.1.mlp.linear_fc1.weight + module.decoder.layers.0.mlp.linear_fc2.weight + module.decoder.layers.0.mlp.linear_fc1.weight + module.decoder.layers.0.self_attention.linear_qkv.layer_norm_bias + module.decoder.final_layernorm.bias + module.decoder.layers.1.mlp.linear_fc2.bias + module.decoder.layers.1.self_attention.linear_qkv.layer_norm_weight + module.decoder.layers.0.self_attention.linear_qkv.bias + module.embedding.position_embeddings.weight +INFO:megatron.core.optimizer:Setting up optimizer with config OptimizerConfig(optimizer='adam', lr=0.0005, min_lr=0.0, decoupled_lr=None, decoupled_min_lr=None, weight_decay=0.1, fp16=True, bf16=False, params_dtype=torch.float16, use_precision_aware_optimizer=False, store_param_remainders=True, main_grads_dtype=torch.float32, main_params_dtype=torch.float32, exp_avg_dtype=torch.float32, exp_avg_sq_dtype=torch.float32, loss_scale=None, initial_loss_scale=4294967296, min_loss_scale=1.0, loss_scale_window=1000, hysteresis=2, adam_beta1=0.9, adam_beta2=0.999, adam_eps=1e-08, sgd_momentum=0.9, use_distributed_optimizer=False, overlap_param_gather_with_optimizer_step=False, optimizer_cpu_offload=False, optimizer_offload_fraction=1.0, use_torch_optimizer_for_cpu_offload=False, overlap_cpu_optimizer_d2h_h2d=False, pin_cpu_grads=True, pin_cpu_params=True, clip_grad=1.0, log_num_zeros_in_grad=False, barrier_with_L1_time=True, timers=, config_logger_dir='') +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (2, 0): 103872000 +>>> embedding +>>> decoder +>>> output_layer +>>> embedding +>>> decoder +>>> output_layer + > number of parameters on (tensor, pipeline) model parallel rank (0, 0): 103872000 + > number of parameters on (tensor, pipeline) model parallel rank (3, 0): 103872000 +INFO:megatron.core.optimizer_param_scheduler:> learning rate decay style: cosine +WARNING: could not find the metadata file gpt-checkpoint/latest_checkpointed_iteration.txt + will not load any checkpoints and will start from random +(min, max) time across ranks (ms): + load-checkpoint ................................: (71.60, 72.25) +[after model, optimizer, and learning rate scheduler are built] datetime: 2025-06-21 20:56:56 +> building train, validation, and test datasets ... + > datasets target sizes (minimum size): + train: 10 + validation: 1 + test: 1 +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let mock = True, as both blend and blend_per_split are None +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split = 1,1,1, an arbitrarily even split, as mock is True +INFO:megatron.core.datasets.blended_megatron_dataset_config:Let split_matrix = [(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)] +> building train, validation, and test datasets for GPT ... +INFO:megatron.core.datasets.blended_megatron_dataset_builder:Building MockGPTDataset splits with sizes=(10, 1, 1) and config=GPTDatasetConfig(random_seed=1234, sequence_length=8192, blend=None, blend_per_split=None, split='1,1,1', split_matrix=[(0, 0.3333333333333333), (0.3333333333333333, 0.6666666666666666), (0.6666666666666666, 1.0)], num_dataset_builder_threads=1, path_to_cache=None, mmap_bin_files=True, mock=True, tokenizer=, mid_level_dataset_surplus=0.005, reset_position_ids=False, reset_attention_mask=False, eod_mask_loss=False, create_attention_mask=True, drop_last_partial_validation_sequence=True, add_extra_token_to_sequence=True, object_storage_cache_path=None) +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset train indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.004686 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8324 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset valid indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.001845 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8320 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +INFO:megatron.core.datasets.gpt_dataset:Build and save the MockGPTDataset test indices +DEBUG:megatron.core.datasets.gpt_dataset:> separate_final_epoch: False +WARNING:megatron.core.datasets.gpt_dataset:Unable to save MockGPTDataset indexes because path_to_cache is None +DEBUG:megatron.core.datasets.gpt_dataset: > time elapsed: 0.001790 seconds +INFO:megatron.core.datasets.gpt_dataset:> total number of samples: 8335 +INFO:megatron.core.datasets.gpt_dataset:> total number of epochs: 1 +> finished creating GPT datasets ... +[after dataloaders are built] datetime: 2025-06-21 20:56:56 +done with setup ... +training ... +(min, max) time across ranks (ms): + model-and-optimizer-setup ......................: (937.64, 967.17) + train/valid/test-data-iterators-setup ..........: (15.79, 144.10) +Setting rerun_state_machine.current_iteration to 0... +[before the start of training step] datetime: 2025-06-21 20:56:56 +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) 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tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) 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torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: 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tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor: tokens torch.Size([4, 32768]) +batch tensor: labels torch.Size([4, 32768]) +batch tensor: 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tensor: labels torch.Size([4, 32768]) +batch tensor: loss_mask torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor: attention_mask torch.Size([4, 1, 32768, 32768]) +batch tensor: position_ids torch.Size([4, 32768]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 1, 8192, 32768]) +batch tensor after cp: position_ids torch.Size([4, 8192]) +batch tensor after cp: tokens torch.Size([4, 8192]) +batch tensor after cp: labels torch.Size([4, 8192]) +batch tensor after cp: loss_mask torch.Size([4, 8192]) +batch tensor after cp: attention_mask torch.Size([4, 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